Highlight Samples

Regex mode off

    Rename Samples

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      Regex mode off

        Explain with AI

        Configure AI settings to get explanations of plots and data in this report.

        Field is required. You can find your API key in the Seqera AI dashboard

        Keys entered here will be stored in your browser's local storage. See the docs.


        Anonymize samples off

        Export Plots

        px
        px
        X

        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


        Choose Plots

        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
        Settings are automatically saved. You can also save named configurations below.

        Save Settings

        You can save the toolbox settings for this report to the browser or as a file.


        Load Settings

        Choose a saved report profile from the browser or load from a file:

          Load from File

        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.28

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-05-10, 17:45 EDT based on data in: /workspace/rnaseq/alignments/hisat2

        Welcome! Not sure where to start?   Watch a tutorial video   (6:06)

        General Statistics

        Showing 6/6 rows and 7/12 columns.
        Sample Name
        Proper Pairs
        rRNA
        mRNA
        Reads
        Reads mapped
        % Reads mapped
        Dups
        GC
        Avg len
        Median len
        Failed
        Seqs
        HBR_Rep1
        98.0%
        0.1%
        82.5%
        0.2M
        0.2M
        99.9%
        6.7%
        50.0%
        100bp
        100bp
        0%
        0.2M
        HBR_Rep2
        98.0%
        0.1%
        83.0%
        0.3M
        0.3M
        99.9%
        6.8%
        50.0%
        100bp
        100bp
        0%
        0.3M
        HBR_Rep3
        98.0%
        0.1%
        82.4%
        0.3M
        0.3M
        99.9%
        6.5%
        50.0%
        100bp
        100bp
        0%
        0.3M
        UHR_Rep1
        95.9%
        0.1%
        84.6%
        0.5M
        0.5M
        99.9%
        10.8%
        49.0%
        100bp
        100bp
        0%
        0.5M
        UHR_Rep2
        95.6%
        0.1%
        78.7%
        0.3M
        0.3M
        99.9%
        10.2%
        49.0%
        100bp
        100bp
        9%
        0.3M
        UHR_Rep3
        95.8%
        0.1%
        84.9%
        0.4M
        0.4M
        99.9%
        11.8%
        49.0%
        100bp
        100bp
        0%
        0.4M

        RSeQC

        Evaluates high throughput RNA-seq data.URL: http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

        Read Distribution

        Read Distribution calculates how mapped reads are distributed over genome features.

        0%20%40%60%80%100%UHR_Rep3UHR_Rep2UHR_Rep1HBR_Rep3HBR_Rep2HBR_Rep1
        CDS_Exons5'UTR_Exons3'UTR_ExonsIntronsTSS_up_1kbTSS_up_1kb-5kbTSS_up_5kb-10kbTES_down_1kbTES_down_1kb-5kbTES_down_5kb-10kbOther_intergenicRSeQC: Read Distribution6 samples# Tags
        Created with MultiQC

        Gene Body Coverage

        Gene Body Coverage calculates read coverage over gene bodies. This is used to check if reads coverage is uniform and if there is any 5' or 3' bias.

        0%20%40%60%80%100%0%20%40%60%80%100%
        RSeQC: Gene Body CoveragePercentages, 6 samplesGene Body Percentile (5' -> 3')Percentage Coverage
        Created with MultiQC

        Inner Distance

        Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

        −200 bp−150 bp−100 bp−50 bp0 bp50 bp100 bp150 bp200 bp02k4k6k8k10k
        RSeQC: Inner DistanceCounts, 6 samplesInner Distance (bp)Counts
        Created with MultiQC

        Junction Saturation

        Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

        Click a line to see the data side by side (as in the original RSeQC plot).

        0%20%40%60%80%100%0500100015002000250030003500
        RSeQC: Junction SaturationAll Junctions, 6 samplesPercent of readsNumber of Junctions
        Created with MultiQC

        Bam Stat

        All numbers reported in millions.

        0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MTotal records 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MQC failed 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MDuplicates 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MNon primary hit 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MUnmapped 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MUnique 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MRead-1 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MRead-2 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45M+ve strand 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45M-ve strand 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MNon-splice reads 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MSplice reads 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MProper pairs 0M0.05M0.1M0.15M0.2M0.25M0.3M0.35M0.4M0.45MDifferent chrom
        RSeQC: Bam Stat6 samples
        Created with MultiQC

        Picard

        Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

        RnaSeqMetrics Assignment

        Number of bases in primary alignments that align to regions in the reference genome.

        05M10M15M20M25M30M35M40M45MUHR_Rep3UHR_Rep2UHR_Rep1HBR_Rep3HBR_Rep2HBR_Rep1
        CodingUTRIntronicIntergenicRibosomalPF not alignedPicard: RnaSeqMetrics Base Assignments6 samplesNumber of bases
        Created with MultiQC

        RnaSeqMetrics Strand Mapping

        Number of aligned reads that map to the correct strand.

        050k100k150k200k250k300k350kUHR_Rep3UHR_Rep2UHR_Rep1HBR_Rep3HBR_Rep2HBR_Rep1
        CorrectIncorrectPicard: RnaSeqMetrics Strand Mapping6 samplesNumber of reads
        Created with MultiQC

        Gene Coverage

        0%20%40%60%80%100%00.20.40.60.811.2
        Picard: Normalized Gene Coverage6 samplesPercent through geneCoverage
        Created with MultiQC

        Samtools

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Flagstat

        This module parses the output from samtools flagstat

        0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MTotal Reads 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MTotal Passed QC 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MMapped 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MSecondary Alignments 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MDuplicates 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MPaired in Sequencing 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MProperly Paired 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MSelf and mate mapped 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MSingletons 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MMate mapped to diff chr 0 M0.05 M0.1 M0.15 M0.2 M0.25 M0.3 M0.35 M0.4 M0.45 MDiff chr (mapQ >= 5) 0%20%40%60%80%100%Total Reads 0%20%40%60%80%100%Total Passed QC 0%20%40%60%80%100%Mapped 0%20%40%60%80%100%Secondary Alignments 0%20%40%60%80%100%Duplicates 0%20%40%60%80%100%Paired in Sequencing 0%20%40%60%80%100%Properly Paired 0%20%40%60%80%100%Self and mate mapped 0%20%40%60%80%100%Singletons 0%20%40%60%80%100%Mate mapped to diff chr 0%20%40%60%80%100%Diff chr (mapQ >= 5)
        Samtools flagstat: read count6 samples
        Created with MultiQC

        FastQC

        Version: 0.11.9

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        050k100k150k200k250k300k350k400k450kUHR_Rep3UHR_Rep2UHR_Rep1HBR_Rep3HBR_Rep2HBR_Rep1
        Unique ReadsDuplicate ReadsFastQC: Sequence Counts6 samplesNumber of reads
        Created with MultiQC

        Sequence Quality Histograms
        6
        0
        0

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        0 bp20 bp40 bp60 bp80 bp100 bp0510152025303540
        FastQC: Mean Quality Scores6 samplesPosition (bp)Phred Score
        Created with MultiQC

        Per Sequence Quality Scores
        6
        0
        0

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        0510152025303540020k40k60k80k100k
        FastQC: Per Sequence Quality Scores6 samplesMean Sequence Quality (Phred Score)Count
        Created with MultiQC

        Per Base Sequence Content
        0
        5
        1

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content
        0
        6
        0

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        0%20%40%60%80%100%0%1%2%3%4%5%
        FastQC: Per Sequence GC ContentPercentages, 6 samples% GCPercentage
        Created with MultiQC

        Per Base N Content
        6
        0
        0

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        0 bp20 bp40 bp60 bp80 bp100 bp0%1%2%3%4%5%
        FastQC: Per Base N Content6 samplesPosition in Read (bp)Percentage N-Count
        Created with MultiQC

        Sequence Length Distribution
        6
        0
        0

        All samples have sequences of a single length (100bp)

        Sequence Duplication Levels
        6
        0
        0

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        123456789>10>50>100>500>1k>5k>10k+0%20%40%60%80%100%
        FastQC: Sequence Duplication Levels6 samplesSequence Duplication Level% of Library
        Created with MultiQC

        Overrepresented sequences by sample
        4
        2
        0

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        6 samples had less than 1% of reads made up of overrepresented sequences

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 1/1 rows and 3/3 columns.
        Overrepresented sequence
        Reports
        Occurrences
        % of all reads
        GTTTATTGAGTGCAGGGAGAAGGGCTTGATGCCTTGGGGTGGGAGGAGAG
        2
        881
        0.0447%

        Adapter Content
        6
        0
        0

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        No samples found with any adapter contamination > 0.1%

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Basic StatisticsPer Base Sequence QualityPer Tile Sequence QualityPer Sequence Quality ScoresPer Base Sequence ContentPer Sequence GC ContentPer Base N ContentSequence Length DistributionSequence Duplication LevelsOverrepresented SequencesAdapter ContentUHR_Rep3UHR_Rep2UHR_Rep1HBR_Rep3HBR_Rep2HBR_Rep1
        FastQC: Status Checks11 samples
        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        FastQC0.11.9